The present disclosure relates generally to question answering computer systems, and more specifically, to learning parameters in a feed forward probabilistic graphical model.
Question answering (QA) is a type of information retrieval. Given a collection of documents, a system employing QA attempts to retrieve answers to questions posed in natural language. QA is regarded as requiring more complex natural language processing (NLP) techniques than other types of information retrieval, such as document retrieval.
An inquiry to a QA system can be in the form a question that includes a single sentence or phrase in natural language (e.g., English) or a formal language (e.g., first order logic) that intends to ask for the end point(s) of a relation or to ask whether or not a relation between two concepts is true. An inquiry can also be in the form of a natural language statement or scenario, which may represent several factors that should be taken into account when searching for an answer. A QA system can model the question and possible answers as an inference model that includes an inference graph.
An inference graph is a feed forward probabilistic graphical model that is useful for formalizing relationships about uncertain events in the world. For example, an inference model with nodes “patient has fever”, “patient has flu”, and “patient is fatigued” can be built. Edges in the graph reflect the way that these events are related; for example, knowing that that a patient has the flu should cause someone to suspect that the patient probably has a fever. In an inference graph the probability of any node is conditionally independent of the probability for all other nodes, given the probability of its parents.